In [1]:
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt
import plotly.express as px

import PV_ICE

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'MESC-NRELStdScens')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines'/'NRELStdScenarios')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
#altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)
In [2]:
print("Python version ", sys.version)
print("Pandas version ", pd.__version__)
print("pyplot ", plt.matplotlib.__version__)
print("PV_ICE version ", PV_ICE.__version__)

Python version  3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]
Pandas version  2.0.3
pyplot  3.7.2
PV_ICE version

Bring in NREL Standard Scenarios data for projection¶

https://scenarioviewer.nrel.gov/ download the data, save it to the NRELStdScenarios folder in baselines.

In [3]:
stdsceninput_raw = pd.read_csv(os.path.join(inputfolder, 'StdScen23_Mid_Case_NoNascent_annual_national.csv'),
           skiprows=[0,1,2,4], header=[0], index_col=1)
In [4]:
stdscen_pv_evens = stdsceninput_raw.filter(like='(MW)').filter(like='PV')
In [5]:
#now make previous odds = next year evens deployment
idx_temp = pd.RangeIndex(start=2024,stop=2051,step=1) #create the index
stdscens_pv_filled = stdscen_pv_evens.reindex(idx_temp, method='bfill')
stdscens_pv_filled
Out[5]:
Behind-the-meter PV capacity (MW) Utility-scale PV capacity (MW)
2024 40722.7 148218.7
2025 53793.8 189992.5
2026 53793.8 189992.5
2027 71560.0 236180.9
2028 71560.0 236180.9
2029 96330.6 257451.0
2030 96330.6 257451.0
2031 115447.6 298806.7
2032 115447.6 298806.7
2033 128290.6 384044.8
2034 128290.6 384044.8
2035 133658.7 495292.0
2036 133658.7 495292.0
2037 138636.6 559092.6
2038 138636.6 559092.6
2039 143451.8 617290.0
2040 143451.8 617290.0
2041 148408.6 691100.5
2042 148408.6 691100.5
2043 154022.7 741715.0
2044 154022.7 741715.0
2045 159586.6 812583.4
2046 159586.6 812583.4
2047 165951.7 866460.9
2048 165951.7 866460.9
2049 173085.5 922887.6
2050 173085.5 922887.6
In [6]:
#stdscens_pv_filled.sum(axis=1)

Set up PV ICE simulation using historical US installs with the NREL Std Scenarios projection¶

In [7]:
MATERIALS = ['glass','aluminium_frames','silver','silicon', 'copper', 'encapsulant', 'backsheet']
moduleFile = os.path.join(baselinesfolder, 'baseline_modules_mass_US_updatedT50T90.csv')
In [9]:
sim1 = PV_ICE.Simulation(name='MESC_StdScen', path=testfolder)
scens = '23_MidCase_NoNascent'
sim1.createScenario(name=scens, massmodulefile=moduleFile) #create the scenario, name and mod file attach
for mat in MATERIALS:
    materialfile = os.path.join(baselinesfolder, 'baseline_material_mass_'+str(mat)+'.csv')
    sim1.scenario[scens].addMaterial(mat, massmatfile=materialfile) # add all materials listed in MATERIALS

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\MESC-NRELStdScens
Baseline folder directed to default:  C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\baselines
No energy module file passed. If desired, pass one of the following options:  ['baseline_modules_energy.csv']
In [10]:
#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value=stdscens_pv_filled.sum(axis=1), start_year=2024) #
In [15]:
plt.plot(sim1.scenario['23_MidCase_NoNascent'].dataIn_m.loc[:,'new_Installed_Capacity_[MW]'])
Out[15]:
[<matplotlib.lines.Line2D at 0x2616da47810>]
In [17]:
sim1.calculateMassFlow()

>>>> Calculating Material Flows <<<<

Working on Scenario:  23_MidCase_NoNascent
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  aluminium_frames
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
In [23]:
ii_yearly, ii_cumu = sim1.aggregateResults()
In [24]:
sim1.plotMetricResults()

["MESC', 'StdScen', '23', 'MidCase', 'NoNascent"]
In [26]:
wasteEoL = ii_yearly['WasteEOL_Module_MESC_StdScen_23_MidCase_NoNascent_[Tonnes]']
decomm = ii_yearly['DecommisionedCapacity_MESC_StdScen_23_MidCase_NoNascent_[MW]']
In [42]:
plt.plot(wasteEoL)
plt.ylim(0,)
plt.xlim(1995,2050)
plt.title('Annual End of Life Module Waste')
plt.ylabel('EoL Module Waste [metric tonnes]')
plt.grid()

So taking the peaks as peak demand for later years for recycling:

  • around 100k metric tonnes by 2030
  • around 300k metric tonnes by 2040
  • around 800k metric tonnes by 2050

Explain the jaggedy nature of the EoL

In [32]:
EoLByMethod_MW = sim1.scenario['23_MidCase_NoNascent'].dataOut_m.filter(like='Yearly_Sum_Power_EOLby_')
EoLByMethod_MW.columns
Out[32]:
Index(['Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure',
       'Yearly_Sum_Power_EOLby_ProjectLifetime'],
      dtype='object')
In [35]:
#plotly plot
fig = px.bar(EoLByMethod_MW, x=EoLByMethod_MW.index, y=EoLByMethod_MW.columns, barmode = 'stack')
fig.show()

Most PV failing before 2050 is due to old installations with shorter project lifetimes.

In [ ]:
In [ ]:
In [ ]: